## Nombre de participants à l'expérimentation :  58
## Nombre de participants se déclarant comme joueurs :  29
## Nombre de femmes se déclarant comme joueuses :  3
## Age médian des joueurs :  15

Removing Outliers based on BET

(pas nécessaire pour la mesure basée sur l’échelle de confiance)

{r removing.outliers.setup.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SETUP # #------------------------------------------------------ # # DTM <- DTAll[which(DTAll$nom_du_jeu=="Motrice"),] # DTL <- DTAll[which(DTAll$nom_du_jeu=="Logique2"),] # DTS <- DTAll[which(DTAll$nom_du_jeu=="Sensoriel"),] # # # get.outliers <- function(DTDescMLoc,DTDescSLoc,DTDescLLoc){ # outliersM <- boxplot.stats(DTDescMLoc$var)$out # outliersS <- boxplot.stats(DTDescSLoc$var)$out # outliersL <- boxplot.stats(DTDescLLoc$var)$out # # outliers = data.table(type=character(0),id=character(0)) # setkey(outliers,id) # if(length(outliersM) > 0) # outliers = merge(outliers,data.table(id=DTDescMLoc[var %in% outliersM]$IDjoueur,type="Moteur"),by=c("id","type"),all=TRUE) # if(length(outliersS) > 0) # outliers = merge(outliers,data.table(id=DTDescSLoc[var %in% outliersS]$IDjoueur,type="Sensoriel"),by=c("id","type"),all=TRUE) # if(length(outliersL) > 0) # outliers = merge(outliers,data.table(id=DTDescLLoc[var %in% outliersL]$IDjoueur,type="Logique"),by=c("id","type"),all=TRUE) # # return(outliers) # } # # plot.outliers <- function(DT,title){ # p <- ggplot(DT, # aes(type,var)) + # xlab("Difficulty Type") + # ylab(title) # p <- p + geom_boxplot() + geom_point(shape=1) # print(p) # } #

{r detect.outliers.bet.sd, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS BET STD DEV # #------------------------------------------------------ # DTDescM = DTM[,.(type="Moteur",var=sd(miseNorm)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=sd(miseNorm)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=sd(miseNorm)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Bet Standard Dev"); # # outliers = get.outliers(DTDescM,DTDescS,DTDescL) # print(paste("Outliers BET STANDARD DEVIATION:",toString(outliers$id))) # # DTM[IDjoueur %in% unlist(outliers[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliers[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Logical Task");NULL},by=.(IDjoueur)] #

{r detect.outliers.win.sum.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUM OF WINS # #------------------------------------------------------ # # Difficulty : win sum # # # DTDescM = DTM[,.(type="Moteur",var=sum(gagnant)),by=IDjoueur] # # DTDescS = DTS[,.(type="Sensoriel",var=sum(gagnant)),by=IDjoueur] # # DTDescL = DTL[,.(type="Logique",var=sum(gagnant)),by=IDjoueur] # # # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win Sum"); # # # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # # print(paste("Outliers :",toString(outliersLoc$id))) # # # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Motor Task");NULL},by=.(IDjoueur)] # # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Sensory Task");NULL},by=.(IDjoueur)] # # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Logical Task");NULL},by=.(IDjoueur)] # #

{r detect.outliers.sheeps.saved.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SAVED SHEEPS # #------------------------------------------------------ # # Difficulty and strategy = saved sheeps # DTDescM = DTM[,.(type="Moteur",var=max(moutons_sauves)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(moutons_sauves)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(moutons_sauves)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Saved sheeps"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET SAVED SHEEPS:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Score Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Score Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Score Logical Task");NULL},by=.(IDjoueur)] # #

{r detect.outliers.dda.exploit.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS EXPLOIT DDA # #------------------------------------------------------ # # DDA Exploit : Win/Fail delta sum max # DTDescM = DTM[,.(type="Moteur",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(cumulDeltaMise)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win/Fail delta sum max"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET EXPLOIT DDA:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Logical Task");NULL},by=.(IDjoueur)] #

{r detect.outliers.summary.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUMMARY # #------------------------------------------------------ # print(paste("Total number of outliers: ",toString(nrow(unique(outliers,by="id"))))) # print(paste("Total number of outliers motor task: ",toString(nrow(unique(outliers[type=="Moteur"],by="id"))))) # print(paste("Total number of outliers perceptive task: ",toString(nrow(unique(outliers[type=="Logique"],by="id"))))) # print(paste("Total number of outliers logical task: ",toString(nrow(unique(outliers[type=="Sensoriel"],by="id"))))) #

{r remove.outliers.bet, echo=FALSE} # #------------------------------------------------------ # # REMOVING OUTLIERS FROM TABLES # #------------------------------------------------------ # # removing all outliers # DTM <- DTM[!IDjoueur %in% unlist(outliers[type=="Moteur"]$id)] # DTS <- DTS[!IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id)] # DTL <- DTL[!IDjoueur %in% unlist(outliers[type=="Logique"]$id)] # DTAll <- data.table() # DTAll <- rbind(DTAll,DTL) # DTAll <- rbind(DTAll,DTM) # DTAll <- rbind(DTAll,DTS) #

Removing Outliers based on CONFIDENCE SCALE

## [1] "Outliers CS STANDARD DEVIATION: 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, dyg7cga2o, dyg7cga2o, ejodnl05c, kctu3te1y, tmxmxmwhi, zp9bc59o5, zv35u39vc"
## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers CS SAVED SHEEPS: "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers CS EXPLOIT DDA: vuq3c2tk6"
## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers:  10"
## [1] "Total number of outliers motor task:  0"
## [1] "Total number of outliers perceptive task:  5"
## [1] "Total number of outliers logical task:  8"

Modeling difficulties

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   2016.5   2038.2  -1004.3   2008.5     1678 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1935 -0.7469  0.2908  0.7381  2.8784 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.559    0.7476  
## Number of obs: 1682, groups:  IDjoueur, 58
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.0580     0.1843   -5.74 9.48e-09 ***
## difficulty    3.0160     0.2115   14.26  < 2e-16 ***
## timeNorm     -0.5213     0.1990   -2.62  0.00879 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.540       
## timeNorm   -0.572 -0.008
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0      1682         0 
## [1] "Player levels from ranef:"
##   (Intercept)      
##  Min.   :-1.05422  
##  1st Qu.:-0.44100  
##  Median :-0.11748  
##  Mean   :-0.00241  
##  3rd Qu.: 0.33077  
##  Max.   : 1.65790  
## [1] "Intercept: -1.06 9.5e-09 ***"
## [1] "Difficulty: 3.02 3.8e-46 ***"
## [1] "Time: -0.521 0.0088 **"
## [1] "R2 fixed: 0.17"
## [1] "R2 mixed: 0.29"
## [1] "Cross Val: 0.69"
## [1] "AIC: 2000"
##         0%        25%        50%        75%       100% 
## -1.6579021 -0.3307656  0.1174780  0.4410031  1.0542161

##         0%        25%        50%        75%       100% 
## -1.6579021 -0.3307656  0.1174780  0.4410031  1.0542161

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1131.6   1152.7   -561.8   1123.6     1446 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.1980 -0.3704  0.1177  0.3458  6.1390 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.7184   0.8476  
## Number of obs: 1450, groups:  IDjoueur, 50
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.1408     0.2668 -11.772   <2e-16 ***
## difficulty    8.0878     0.4208  19.219   <2e-16 ***
## timeNorm     -0.4433     0.2833  -1.565    0.118    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.631       
## timeNorm   -0.511 -0.077
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0         0      1450 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.666981  
##  1st Qu.:-0.446178  
##  Median : 0.061001  
##  Mean   :-0.001145  
##  3rd Qu.: 0.422346  
##  Max.   : 1.471194  
## [1] "Intercept: -3.14 5.5e-32 ***"
## [1] "Difficulty: 8.09 2.6e-82 ***"
## [1] "Time: -0.443 0.12 :("
## [1] "R2 fixed: 0.3"
## [1] "R2 mixed: 0.46"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1100"
##          0%         25%         50%         75%        100% 
## -1.47119372 -0.42234637 -0.06100097  0.44617818  1.66698065

##          0%         25%         50%         75%        100% 
## -1.47119372 -0.42234637 -0.06100097  0.44617818  1.66698065

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1444.5   1465.8   -718.2   1436.5     1533 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.0357 -0.4980 -0.1017  0.5004  5.0622 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.57     1.253   
## Number of obs: 1537, groups:  IDjoueur, 53
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.9054     0.2628  -7.251 4.14e-13 ***
## difficulty    5.7562     0.3198  18.001  < 2e-16 ***
## timeNorm     -1.9355     0.2564  -7.550 4.35e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.497       
## timeNorm   -0.376 -0.233
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##      1537         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.8051717  
##  1st Qu.:-0.7513212  
##  Median :-0.2064150  
##  Mean   :-0.0003176  
##  3rd Qu.: 0.7228639  
##  Max.   : 3.1492300  
## [1] "Intercept: -1.91 4.1e-13 ***"
## [1] "Difficulty: 5.76 1.9e-72 ***"
## [1] "Time: -1.94 4.4e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1400"
##         0%        25%        50%        75%       100% 
## -3.1492300 -0.7228639  0.2064150  0.7513212  1.8051717

##         0%        25%        50%        75%       100% 
## -3.1492300 -0.7228639  0.2064150  0.7513212  1.8051717

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'gam'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3393, p-value = 0.1805
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1375478

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.0196, p-value = 0.3079
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1132275

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.12965, p-value = 0.8968
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.01388433

Playing board games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.86388, p-value = 0.3877
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.08757052

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.61918, p-value = 0.5358
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.0679803

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.6523, p-value = 0.5142
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.06919576

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 29 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.16967, p-value = 0.8653
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02270513
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 24 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.1307, p-value = 0.03311
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##      tau 
## 0.304417 
## 
## [1] "self.eff.on.level.s 0.3 0.033 *"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 27 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.46598, p-value = 0.6412
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.06648267

Risk aversion and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3157, p-value = 0.1883
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##      tau 
## 0.127906

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.3373, p-value = 0.01943
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2455088 
## 
## [1] "risk.av.on.level.s 0.25 0.019 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3781, p-value = 0.1682
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1404273

Age and level for each task

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.1261, p-value = 0.2601
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1063448
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 1 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.8528, p-value = 0.06391
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##      tau 
## 0.189264 
## 
## [1] "age.on.level.s 0.19 0.064 ."
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.1451, p-value = 0.2522
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1130316

Sex and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.3774, p-value = 0.01743
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2593202 
## 
## [1] "sexe.on.level.m -0.26 0.017 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.18718, p-value = 0.8515
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02204982

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.38949, p-value = 0.6969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.04451521

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 227, p-value = 0.01687
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.8465888 -0.1080105
## sample estimates:
## difference in location 
##             -0.4966452 
## 
## [1] "sexe.on.level.m.2 -0.5 0.017 * mean(A): 0.16 mean(B): -0.32"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 281, p-value = 0.8612
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.4166427  0.5657028
## sample estimates:
## difference in location 
##             0.02816739

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 302, p-value = 0.7064
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.7753238  0.5708569
## sample estimates:
## difference in location 
##            -0.06017729

CONFIDENCE SCALE APPROACH

For Bet approach, see the other file.

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0790 43 0.00098 ***
##  2:      0.09375         0.1200 55 4.5e-05 ***
##  3:      0.15625         0.1100 57 0.00023 ***
##  4:      0.21875         0.1500 58 1.1e-06 ***
##  5:      0.28125         0.1200 56 9.3e-05 ***
##  6:      0.34375         0.1100 57 2.5e-05 ***
##  7:      0.40625         0.0830 56     0.014 *
##  8:      0.46875         0.0150 57     0.48 :(
##  9:      0.53125        -0.0063 55     0.56 :(
## 10:      0.59375        -0.0600 58   0.0022 **
## 11:      0.65625        -0.0980 58 7.7e-05 ***
## 12:      0.71875        -0.1200 57 3.6e-06 ***
## 13:      0.78125        -0.1700 55 1.3e-07 ***
## 14:      0.84375        -0.2200 52 1.8e-08 ***
## 15:      0.90625        -0.2300 55 4.2e-10 ***
## 16:      0.96875        -0.1800 55 1.3e-09 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 43 0.00098 ***
##  2: 55 4.5e-05 ***
##  3: 57 0.00023 ***
##  4: 58 1.1e-06 ***
##  5: 56 9.3e-05 ***
##  6: 57 2.5e-05 ***
##  7: 56     0.014 *
##  8: 57     0.48 :(
##  9: 55     0.56 :(
## 10: 58   0.0022 **
## 11: 58 7.7e-05 ***
## 12: 57 3.6e-06 ***
## 13: 55 1.3e-07 ***
## 14: 52 1.8e-08 ***
## 15: 55 4.2e-10 ***
## 16: 55 1.3e-09 ***
## [1] 55.2
## [1] 3.62

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.080 33   0.0081 **
##  2:      0.09375          0.140 39 0.00015 ***
##  3:      0.15625          0.120 44 0.00052 ***
##  4:      0.21875          0.150 43 6.6e-05 ***
##  5:      0.28125          0.140 39   0.0013 **
##  6:      0.34375          0.120 39 9.6e-05 ***
##  7:      0.40625          0.094 41   0.0062 **
##  8:      0.46875          0.031 39     0.049 *
##  9:      0.53125          0.010 40     0.67 :(
## 10:      0.59375         -0.044 43     0.051 .
## 11:      0.65625         -0.110 40     0.013 *
## 12:      0.71875         -0.140 40 8.8e-05 ***
## 13:      0.78125         -0.170 37 0.00035 ***
## 14:      0.84375         -0.240 29 2.1e-05 ***
## 15:      0.90625         -0.230 29 1.1e-05 ***
## 16:      0.96875         -0.210 17   0.0038 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 33   0.0081 **
##  2: 39 0.00015 ***
##  3: 44 0.00052 ***
##  4: 43 6.6e-05 ***
##  5: 39   0.0013 **
##  6: 39 9.6e-05 ***
##  7: 41   0.0062 **
##  8: 39     0.049 *
##  9: 40     0.67 :(
## 10: 43     0.051 .
## 11: 40     0.013 *
## 12: 40 8.8e-05 ***
## 13: 37 0.00035 ***
## 14: 29 2.1e-05 ***
## 15: 29 1.1e-05 ***
## 16: 17   0.0038 **
## [1] 37
## [1] 6.95

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.052 27     0.093 .
##  2:      0.09375          0.077 30     0.064 .
##  3:      0.15625          0.031 28     0.59 :(
##  4:      0.21875          0.081 35     0.011 *
##  5:      0.28125          0.110 36      0.04 *
##  6:      0.34375          0.056 36     0.093 .
##  7:      0.40625          0.069 39     0.29 :(
##  8:      0.46875         -0.019 38     0.82 :(
##  9:      0.53125          0.019 36     0.82 :(
## 10:      0.59375         -0.081 38     0.036 *
## 11:      0.65625         -0.120 40     0.013 *
## 12:      0.71875         -0.090 38   0.0053 **
## 13:      0.78125         -0.130 41 0.00024 ***
## 14:      0.84375         -0.230 39 7.5e-06 ***
## 15:      0.90625         -0.210 40 4.7e-07 ***
## 16:      0.96875         -0.170 37 9.3e-07 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 27     0.093 .
##  2: 30     0.064 .
##  3: 28     0.59 :(
##  4: 35     0.011 *
##  5: 36      0.04 *
##  6: 36     0.093 .
##  7: 39     0.29 :(
##  8: 38     0.82 :(
##  9: 36     0.82 :(
## 10: 38     0.036 *
## 11: 40     0.013 *
## 12: 38   0.0053 **
## 13: 41 0.00024 ***
## 14: 39 7.5e-06 ***
## 15: 40 4.7e-07 ***
## 16: 37 9.3e-07 ***
## [1] 36.1
## [1] 4.24

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.019  2        1 :(
##  2:      0.09375          0.081 11     0.12 :(
##  3:      0.15625          0.120 13      0.03 *
##  4:      0.21875          0.089  8     0.29 :(
##  5:      0.28125          0.069  9     0.81 :(
##  6:      0.34375          0.130  7     0.048 *
##  7:      0.40625          0.094  9     0.28 :(
##  8:      0.46875          0.031 10     0.75 :(
##  9:      0.53125         -0.200 13   0.0072 **
## 10:      0.59375         -0.094 15     0.056 .
## 11:      0.65625         -0.160 15     0.076 .
## 12:      0.71875         -0.150 14      0.05 .
## 13:      0.78125         -0.160 14   0.0094 **
## 14:      0.84375         -0.240 16   0.0027 **
## 15:      0.90625         -0.210 18   0.0012 **
## 16:      0.96875         -0.250 17 0.00037 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  2        1 :(
##  2: 11     0.12 :(
##  3: 13      0.03 *
##  4:  8     0.29 :(
##  5:  9     0.81 :(
##  6:  7     0.048 *
##  7:  9     0.28 :(
##  8: 10     0.75 :(
##  9: 13   0.0072 **
## 10: 15     0.056 .
## 11: 15     0.076 .
## 12: 14      0.05 .
## 13: 14   0.0094 **
## 14: 16   0.0027 **
## 15: 18   0.0012 **
## 16: 17 0.00037 ***
## [1] 11.9
## [1] 4.23

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375          0.094  9     0.63 :(
##  3:      0.15625          0.094 29     0.43 :(
##  4:      0.21875          0.069 41     0.037 *
##  5:      0.28125          0.094 47     0.018 *
##  6:      0.34375          0.110 50     0.013 *
##  7:      0.40625          0.069 50     0.074 .
##  8:      0.46875          0.040 51     0.036 *
##  9:      0.53125          0.035 54     0.15 :(
## 10:      0.59375         -0.029 53     0.41 :(
## 11:      0.65625         -0.081 54   0.0085 **
## 12:      0.71875         -0.069 54   0.0029 **
## 13:      0.78125         -0.110 45 0.00073 ***
## 14:      0.84375         -0.170 29   0.0045 **
## 15:      0.90625         -0.210 15     0.018 *
## 16:      0.96875         -0.270  6     0.056 .
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9     0.63 :(
##  2: 29     0.43 :(
##  3: 41     0.037 *
##  4: 47     0.018 *
##  5: 50     0.013 *
##  6: 50     0.074 .
##  7: 51     0.036 *
##  8: 54     0.15 :(
##  9: 53     0.41 :(
## 10: 54   0.0085 **
## 11: 54   0.0029 **
## 12: 45 0.00073 ***
## 13: 29   0.0045 **
## 14: 15     0.018 *
## 15:  6     0.056 .
## [1] 39.1
## [1] 17.2
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375         0.0940  9   0.63 :(
##  3:      0.15625         0.0940 26    0.4 :(
##  4:      0.21875         0.0790 27   0.073 .
##  5:      0.28125         0.1200 25   0.017 *
##  6:      0.34375         0.1100 27 0.0014 **
##  7:      0.40625         0.0690 26   0.032 *
##  8:      0.46875         0.0810 25 0.0095 **
##  9:      0.53125         0.0690 25   0.14 :(
## 10:      0.59375         0.0062 24   0.92 :(
## 11:      0.65625        -0.0400 25   0.33 :(
## 12:      0.71875        -0.0880 24   0.023 *
## 13:      0.78125        -0.0810 15   0.037 *
## 14:      0.84375             NA  0        NA
## 15:      0.90625             NA  0        NA
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  9   0.63 :(
##  2: 26    0.4 :(
##  3: 27   0.073 .
##  4: 25   0.017 *
##  5: 27 0.0014 **
##  6: 26   0.032 *
##  7: 25 0.0095 **
##  8: 25   0.14 :(
##  9: 24   0.92 :(
## 10: 25   0.33 :(
## 11: 24   0.023 *
## 12: 15   0.037 *
## [1] 23.2
## [1] 5.46
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375             NA  0      NA
##  3:      0.15625             NA  3      NA
##  4:      0.21875         0.0670 14 0.29 :(
##  5:      0.28125         0.0690 21 0.31 :(
##  6:      0.34375         0.0460 22 0.67 :(
##  7:      0.40625         0.0190 22 0.92 :(
##  8:      0.46875        -0.0021 22 0.97 :(
##  9:      0.53125         0.0350 22 0.24 :(
## 10:      0.59375        -0.0770 22  0.2 :(
## 11:      0.65625        -0.1200 22 0.019 *
## 12:      0.71875        -0.0440 23 0.17 :(
## 13:      0.78125        -0.0810 22 0.079 .
## 14:      0.84375        -0.1800 21 0.024 *
## 15:      0.90625        -0.1900  7 0.15 :(
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 14 0.29 :(
##  2: 21 0.31 :(
##  3: 22 0.67 :(
##  4: 22 0.92 :(
##  5: 22 0.97 :(
##  6: 22 0.24 :(
##  7: 22  0.2 :(
##  8: 22 0.019 *
##  9: 23 0.17 :(
## 10: 22 0.079 .
## 11: 21 0.024 *
## 12:  7 0.15 :(
## [1] 20
## [1] 4.71
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 1      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625          0.190 2  0.5 :(
##  8:      0.46875             NA 4      NA
##  9:      0.53125         -0.031 7 0.19 :(
## 10:      0.59375         -0.094 7 0.33 :(
## 11:      0.65625         -0.160 7 0.33 :(
## 12:      0.71875         -0.085 7 0.15 :(
## 13:      0.78125         -0.180 8 0.028 *
## 14:      0.84375         -0.160 8  0.1 :(
## 15:      0.90625         -0.210 8 0.055 .
## 16:      0.96875         -0.270 6 0.056 .
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  2  0.5 :(
## 2:  7 0.19 :(
## 3:  7 0.33 :(
## 4:  7 0.33 :(
## 5:  7 0.15 :(
## 6:  8 0.028 *
## 7:  8  0.1 :(
## 8:  8 0.055 .
## 9:  6 0.056 .
## [1] 6.67
## [1] 1.87
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0520 38     0.16 :(
##  2:      0.09375         0.0310 47      0.2 :(
##  3:      0.15625         0.0770 46     0.31 :(
##  4:      0.21875         0.0310 34      0.3 :(
##  5:      0.28125        -0.0063 36     0.87 :(
##  6:      0.34375        -0.0190 29     0.74 :(
##  7:      0.40625        -0.0310 31     0.62 :(
##  8:      0.46875        -0.1400 31     0.019 *
##  9:      0.53125        -0.1600 27   0.0065 **
## 10:      0.59375        -0.1900 34    0.001 **
## 11:      0.65625        -0.1600 35   0.0012 **
## 12:      0.71875        -0.2200 34 0.00012 ***
## 13:      0.78125        -0.2300 32 4.5e-05 ***
## 14:      0.84375        -0.2400 40 1.3e-05 ***
## 15:      0.90625        -0.2000 48 5.5e-08 ***
## 16:      0.96875        -0.0950 50 6.8e-07 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 38     0.16 :(
##  2: 47      0.2 :(
##  3: 46     0.31 :(
##  4: 34      0.3 :(
##  5: 36     0.87 :(
##  6: 29     0.74 :(
##  7: 31     0.62 :(
##  8: 31     0.019 *
##  9: 27   0.0065 **
## 10: 34    0.001 **
## 11: 35   0.0012 **
## 12: 34 0.00012 ***
## 13: 32 4.5e-05 ***
## 14: 40 1.3e-05 ***
## 15: 48 5.5e-08 ***
## 16: 50 6.8e-07 ***
## [1] 37
## [1] 7.18

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125          0.060 10 0.41 :(
##  2:      0.09375         -0.044  9 0.63 :(
##  3:      0.15625         -0.056 10    1 :(
##  4:      0.21875         -0.044  4 0.88 :(
##  5:      0.28125          0.057  8 0.73 :(
##  6:      0.34375         -0.190  5 0.18 :(
##  7:      0.40625         -0.210  6 0.093 .
##  8:      0.46875         -0.320  7 0.11 :(
##  9:      0.53125         -0.160  5 0.44 :(
## 10:      0.59375         -0.250  7 0.11 :(
## 11:      0.65625         -0.360  6 0.031 *
## 12:      0.71875         -0.440  8 0.013 *
## 13:      0.78125         -0.260  5 0.31 :(
## 14:      0.84375         -0.190  8 0.14 :(
## 15:      0.90625         -0.110  9 0.024 *
## 16:      0.96875         -0.024 10 0.41 :(
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 10 0.41 :(
##  2:  9 0.63 :(
##  3: 10    1 :(
##  4:  4 0.88 :(
##  5:  8 0.73 :(
##  6:  5 0.18 :(
##  7:  6 0.093 .
##  8:  7 0.11 :(
##  9:  5 0.44 :(
## 10:  7 0.11 :(
## 11:  6 0.031 *
## 12:  8 0.013 *
## 13:  5 0.31 :(
## 14:  8 0.14 :(
## 15:  9 0.024 *
## 16: 10 0.41 :(
## [1] 7.31
## [1] 1.99

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0520 26     0.31 :(
##  2:      0.09375         0.0310 27     0.47 :(
##  3:      0.15625        -0.0063 23     0.66 :(
##  4:      0.21875         0.0310 22     0.51 :(
##  5:      0.28125        -0.0310 19     0.73 :(
##  6:      0.34375        -0.0190 18     0.79 :(
##  7:      0.40625         0.0190 18     0.79 :(
##  8:      0.46875        -0.0690 18     0.24 :(
##  9:      0.53125        -0.1300 16      0.1 :(
## 10:      0.59375        -0.2400 18     0.021 *
## 11:      0.65625        -0.1600 20     0.021 *
## 12:      0.71875        -0.2200 16    0.004 **
## 13:      0.78125        -0.2300 20 0.00052 ***
## 14:      0.84375        -0.3400 21   0.0012 **
## 15:      0.90625        -0.2100 26 4.8e-05 ***
## 16:      0.96875        -0.0780 27   2e-04 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 26     0.31 :(
##  2: 27     0.47 :(
##  3: 23     0.66 :(
##  4: 22     0.51 :(
##  5: 19     0.73 :(
##  6: 18     0.79 :(
##  7: 18     0.79 :(
##  8: 18     0.24 :(
##  9: 16      0.1 :(
## 10: 18     0.021 *
## 11: 20     0.021 *
## 12: 16    0.004 **
## 13: 20 0.00052 ***
## 14: 21   0.0012 **
## 15: 26 4.8e-05 ***
## 16: 27   2e-04 ***
## [1] 20.9
## [1] 3.82

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.019  2      1 :(
##  2:      0.09375          0.081 11   0.12 :(
##  3:      0.15625          0.120 13    0.03 *
##  4:      0.21875          0.089  8   0.29 :(
##  5:      0.28125         -0.031  9      1 :(
##  6:      0.34375          0.090  6    0.09 .
##  7:      0.40625          0.094  7   0.55 :(
##  8:      0.46875         -0.120  6    0.4 :(
##  9:      0.53125         -0.280  6   0.034 *
## 10:      0.59375         -0.094  9   0.091 .
## 11:      0.65625         -0.110  9    0.4 :(
## 12:      0.71875         -0.094 10   0.47 :(
## 13:      0.78125         -0.220  7   0.11 :(
## 14:      0.84375         -0.210 11   0.023 *
## 15:      0.90625         -0.250 13 0.0051 **
## 16:      0.96875         -0.200 13 0.0021 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  2      1 :(
##  2: 11   0.12 :(
##  3: 13    0.03 *
##  4:  8   0.29 :(
##  5:  9      1 :(
##  6:  6    0.09 .
##  7:  7   0.55 :(
##  8:  6    0.4 :(
##  9:  6   0.034 *
## 10:  9   0.091 .
## 11:  9    0.4 :(
## 12: 10   0.47 :(
## 13:  7   0.11 :(
## 14: 11   0.023 *
## 15: 13 0.0051 **
## 16: 13 0.0021 **
## [1] 8.75
## [1] 3.07

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.094 36   0.0044 **
##  2:      0.09375          0.160 41 3.1e-05 ***
##  3:      0.15625          0.170 42 8.4e-05 ***
##  4:      0.21875          0.260 44 3.2e-06 ***
##  5:      0.28125          0.220 36 0.00012 ***
##  6:      0.34375          0.160 40 5.4e-05 ***
##  7:      0.40625          0.094 44   0.0061 **
##  8:      0.46875          0.031 41     0.038 *
##  9:      0.53125         -0.031 38      0.5 :(
## 10:      0.59375         -0.044 42     0.41 :(
## 11:      0.65625         -0.056 40     0.46 :(
## 12:      0.71875         -0.069 39   0.0097 **
## 13:      0.78125         -0.150 44 0.00022 ***
## 14:      0.84375         -0.230 43 2.1e-07 ***
## 15:      0.90625         -0.260 42 4.7e-07 ***
## 16:      0.96875         -0.350 27 6.1e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 36   0.0044 **
##  2: 41 3.1e-05 ***
##  3: 42 8.4e-05 ***
##  4: 44 3.2e-06 ***
##  5: 36 0.00012 ***
##  6: 40 5.4e-05 ***
##  7: 44   0.0061 **
##  8: 41     0.038 *
##  9: 38      0.5 :(
## 10: 42     0.41 :(
## 11: 40     0.46 :(
## 12: 39   0.0097 **
## 13: 44 0.00022 ***
## 14: 43 2.1e-07 ***
## 15: 42 4.7e-07 ***
## 16: 27 6.1e-06 ***
## [1] 39.9
## [1] 4.3

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.089 32   0.0067 **
##  2:      0.09375          0.160 35 0.00012 ***
##  3:      0.15625          0.160 33 0.00055 ***
##  4:      0.21875          0.240 32 5.8e-05 ***
##  5:      0.28125          0.190 26   0.0034 **
##  6:      0.34375          0.160 27   0.0052 **
##  7:      0.40625          0.094 29     0.039 *
##  8:      0.46875          0.031 28     0.064 .
##  9:      0.53125         -0.031 27     0.67 :(
## 10:      0.59375         -0.060 28     0.12 :(
## 11:      0.65625         -0.110 26     0.17 :(
## 12:      0.71875         -0.100 25     0.021 *
## 13:      0.78125         -0.160 29   0.0024 **
## 14:      0.84375         -0.240 27 4.3e-05 ***
## 15:      0.90625         -0.310 24 0.00012 ***
## 16:      0.96875         -0.340  9     0.012 *
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 32   0.0067 **
##  2: 35 0.00012 ***
##  3: 33 0.00055 ***
##  4: 32 5.8e-05 ***
##  5: 26   0.0034 **
##  6: 27   0.0052 **
##  7: 29     0.039 *
##  8: 28     0.064 .
##  9: 27     0.67 :(
## 10: 28     0.12 :(
## 11: 26     0.17 :(
## 12: 25     0.021 *
## 13: 29   0.0024 **
## 14: 27 4.3e-05 ***
## 15: 24 0.00012 ***
## 16:  9     0.012 *
## [1] 27.3
## [1] 5.76

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.190  4     0.58 :(
##  2:      0.09375          0.360  6     0.14 :(
##  3:      0.15625          0.170  9     0.053 .
##  4:      0.21875          0.270 12      0.02 *
##  5:      0.28125          0.220 10   0.0098 **
##  6:      0.34375          0.180 12   0.0037 **
##  7:      0.40625          0.170 14     0.077 .
##  8:      0.46875          0.031 13      0.4 :(
##  9:      0.53125         -0.031 10     0.84 :(
## 10:      0.59375          0.031 13      0.4 :(
## 11:      0.65625          0.069 13     0.44 :(
## 12:      0.71875         -0.019 13     0.43 :(
## 13:      0.78125         -0.150 15     0.043 *
## 14:      0.84375         -0.230 16   0.0014 **
## 15:      0.90625         -0.210 17    0.002 **
## 16:      0.96875         -0.350 17 0.00031 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  4     0.58 :(
##  2:  6     0.14 :(
##  3:  9     0.053 .
##  4: 12      0.02 *
##  5: 10   0.0098 **
##  6: 12   0.0037 **
##  7: 14     0.077 .
##  8: 13      0.4 :(
##  9: 10     0.84 :(
## 10: 13      0.4 :(
## 11: 13     0.44 :(
## 12: 13     0.43 :(
## 13: 15     0.043 *
## 14: 16   0.0014 **
## 15: 17    0.002 **
## 16: 17 0.00031 ***
## [1] 12.1
## [1] 3.65

## [1] "bad"

##     obj.diff.bin delta.obj.subj n pval
##  1:      0.03125             NA 0   NA
##  2:      0.09375             NA 0   NA
##  3:      0.15625             NA 0   NA
##  4:      0.21875             NA 0   NA
##  5:      0.28125             NA 0   NA
##  6:      0.34375             NA 1   NA
##  7:      0.40625             NA 1   NA
##  8:      0.46875             NA 0   NA
##  9:      0.53125             NA 1   NA
## 10:      0.59375             NA 1   NA
## 11:      0.65625             NA 1   NA
## 12:      0.71875             NA 1   NA
## 13:      0.78125             NA 0   NA
## 14:      0.84375             NA 0   NA
## 15:      0.90625             NA 1   NA
## 16:      0.96875             NA 1   NA
## [1] "mean and sd of nb players per bin"
## Empty data.table (0 rows) of 2 cols: nb,pval
## [1] NaN
## [1] NA
## Warning: Removed 16 rows containing missing values (geom_point).
## Warning: Removed 16 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71195  -0.16836   0.00376   0.17619   0.63833  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.182297   0.019774   9.219   <2e-16 ***
## timeNorm     0.005893   0.020913   0.282    0.778    
## obj.diff    -0.375586   0.025858 -14.525   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05568596)
## 
##     Null deviance: 105.649  on 1681  degrees of freedom
## Residual deviance:  93.497  on 1679  degrees of freedom
## AIC: -79.355
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.81832  -0.18077  -0.03144   0.21140   0.81844  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.04638    0.01869   2.482   0.0132 *  
## timeNorm     0.04996    0.02477   2.017   0.0439 *  
## obj.diff    -0.27350    0.01929 -14.177   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06919415)
## 
##     Null deviance: 114.36  on 1449  degrees of freedom
## Residual deviance: 100.12  on 1447  degrees of freedom
## AIC: 247.2
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73430  -0.20594  -0.01949   0.19850   0.71398  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.21759    0.02001   10.88   <2e-16 ***
## timeNorm     0.05914    0.02495    2.37   0.0179 *  
## obj.diff    -0.53045    0.02119  -25.04   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06995631)
## 
##     Null deviance: 156.54  on 1536  degrees of freedom
## Residual deviance: 107.31  on 1534  degrees of freedom
## AIC: 278.57
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean    error.diff   n    pval
##  1:      1.5      0.5422414     0.6014885 -0.0544661582 116 0.038 *
##  2:      4.5      0.5367816     0.5712048 -0.0274664169 174 0.17 :(
##  3:      7.5      0.5155172     0.5413406 -0.0217582878 174 0.28 :(
##  4:     10.5      0.5413793     0.5404361  0.0102982443 174 0.62 :(
##  5:     13.5      0.5155172     0.5181081 -0.0005152110 174 0.97 :(
##  6:     16.5      0.5310345     0.5333167 -0.0007660154 174 0.97 :(
##  7:     19.5      0.5063218     0.5344527 -0.0290711237 174 0.12 :(
##  8:     22.5      0.4873563     0.4934513 -0.0053069701 174  0.8 :(
##  9:     25.5      0.4890805     0.4822968  0.0047959969 174  0.8 :(
## 10:     28.5      0.4741379     0.4548030  0.0173421720 174  0.4 :(
##     time    error.diff shapes
##  1:  1.5 -0.0544661582     24
##  2:  4.5 -0.0274664169     16
##  3:  7.5 -0.0217582878     16
##  4: 10.5  0.0102982443     16
##  5: 13.5 -0.0005152110     16
##  6: 16.5 -0.0007660154     16
##  7: 19.5 -0.0290711237     16
##  8: 22.5 -0.0053069701     16
##  9: 25.5  0.0047959969     16
## 10: 28.5  0.0173421720     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4700000     0.5997864 -0.13975600 100 3.6e-05 ***
##  2:      4.5      0.5140000     0.6275717 -0.09648920 150 3.6e-07 ***
##  3:      7.5      0.4680000     0.5393273 -0.07378323 150 0.00087 ***
##  4:     10.5      0.5200000     0.5921954 -0.06766340 150 0.00055 ***
##  5:     13.5      0.4733333     0.5792956 -0.09407597 150 4.9e-07 ***
##  6:     16.5      0.4246667     0.5271146 -0.10651377 150 4.4e-06 ***
##  7:     19.5      0.4820000     0.5466728 -0.05111224 150   0.0024 **
##  8:     22.5      0.5026667     0.5836131 -0.06894162 150 0.00099 ***
##  9:     25.5      0.5526667     0.6047896 -0.03482444 150     0.041 *
## 10:     28.5      0.5046667     0.5734270 -0.06386275 150   0.0018 **
##     time  error.diff shapes
##  1:  1.5 -0.13975600     24
##  2:  4.5 -0.09648920     24
##  3:  7.5 -0.07378323     24
##  4: 10.5 -0.06766340     24
##  5: 13.5 -0.09407597     24
##  6: 16.5 -0.10651377     24
##  7: 19.5 -0.05111224     24
##  8: 22.5 -0.06894162     24
##  9: 25.5 -0.03482444     24
## 10: 28.5 -0.06386275     24

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n        pval
##  1:      1.5      0.4415094     0.6007697 -1.658770e-01 106 3.8e-06 ***
##  2:      4.5      0.5119497     0.6324837 -1.343840e-01 159 4.2e-06 ***
##  3:      7.5      0.5100629     0.5479813 -4.895619e-02 159     0.069 .
##  4:     10.5      0.5220126     0.5177334  2.196993e-03 159     0.93 :(
##  5:     13.5      0.5169811     0.5303606 -2.035258e-02 159     0.43 :(
##  6:     16.5      0.5100629     0.5026471  2.226322e-05 159        1 :(
##  7:     19.5      0.4584906     0.4514766 -3.401739e-03 159     0.87 :(
##  8:     22.5      0.4226415     0.4287566 -1.335901e-02 159      0.6 :(
##  9:     25.5      0.4584906     0.3964332  6.936761e-02 159     0.013 *
## 10:     28.5      0.4446541     0.3652666  6.326623e-02 159     0.012 *
##     time    error.diff shapes
##  1:  1.5 -1.658770e-01     24
##  2:  4.5 -1.343840e-01     24
##  3:  7.5 -4.895619e-02     16
##  4: 10.5  2.196993e-03     16
##  5: 13.5 -2.035258e-02     16
##  6: 16.5  2.226322e-05     16
##  7: 19.5 -3.401739e-03     16
##  8: 22.5 -1.335901e-02     16
##  9: 25.5  6.936761e-02     24
## 10: 28.5  6.326623e-02     24

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.77343  -0.19818  -0.03673   0.23177   0.65313  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.14977    0.03252   4.605 4.98e-06 ***
## timeNorm     0.06656    0.03372   1.974   0.0488 *  
## obj.diff    -0.43846    0.03367 -13.023  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05617837)
## 
##     Null deviance: 45.656  on 637  degrees of freedom
## Residual deviance: 35.673  on 635  degrees of freedom
## AIC: -21.386
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.76993  -0.21307  -0.01747   0.22174   0.78086  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.13284    0.01937   6.857 9.44e-12 ***
## timeNorm     0.06293    0.02244   2.804  0.00509 ** 
## obj.diff    -0.38451    0.02115 -18.182  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0751367)
## 
##     Null deviance: 172.40  on 1942  degrees of freedom
## Residual deviance: 145.77  on 1940  degrees of freedom
## AIC: 489.64
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.68555  -0.19806  -0.00524   0.19853   0.77278  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.15257    0.01553   9.827   <2e-16 ***
## timeNorm     0.03553    0.01977   1.797   0.0725 .  
## obj.diff    -0.36613    0.01986 -18.438   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06099778)
## 
##     Null deviance: 149.80  on 2087  degrees of freedom
## Residual deviance: 127.18  on 2085  degrees of freedom
## AIC: 90.521
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5431818     0.7360044 -0.20618414 44 1.8e-05 ***
##  2:      4.5      0.5772727     0.7437994 -0.18573742 66 3.8e-05 ***
##  3:      7.5      0.5863636     0.7366649 -0.16092775 66 3.4e-05 ***
##  4:     10.5      0.6151515     0.7435502 -0.13979941 66 0.00045 ***
##  5:     13.5      0.6136364     0.7549140 -0.14937585 66 1.4e-05 ***
##  6:     16.5      0.5439394     0.6892211 -0.16497802 66   6e-05 ***
##  7:     19.5      0.5954545     0.6876110 -0.09995108 66   0.0045 **
##  8:     22.5      0.6500000     0.7543181 -0.10352490 66     0.016 *
##  9:     25.5      0.5803030     0.7062212 -0.11223014 66   0.0014 **
## 10:     28.5      0.5772727     0.6571965 -0.06694013 66     0.066 .
##     time  error.diff shapes
##  1:  1.5 -0.20618414     24
##  2:  4.5 -0.18573742     24
##  3:  7.5 -0.16092775     24
##  4: 10.5 -0.13979941     24
##  5: 13.5 -0.14937585     24
##  6: 16.5 -0.16497802     24
##  7: 19.5 -0.09995108     24
##  8: 22.5 -0.10352490     24
##  9: 25.5 -0.11223014     24
## 10: 28.5 -0.06694013     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.5126866     0.6582011 -0.151085119 134 1.6e-06 ***
##  2:      4.5      0.5527363     0.6859782 -0.131021735 201 1.2e-09 ***
##  3:      7.5      0.5258706     0.5752083 -0.052696624 201     0.013 *
##  4:     10.5      0.5646766     0.6181909 -0.052068821 201     0.023 *
##  5:     13.5      0.5378109     0.5945723 -0.057534745 201    0.005 **
##  6:     16.5      0.5348259     0.5936144 -0.057881841 201   0.0064 **
##  7:     19.5      0.5318408     0.5954009 -0.062124974 201    0.001 **
##  8:     22.5      0.4756219     0.5528899 -0.085500161 201 0.00017 ***
##  9:     25.5      0.5587065     0.5651688 -0.008144267 201     0.69 :(
## 10:     28.5      0.5368159     0.5513390 -0.027193375 201     0.15 :(
##     time   error.diff shapes
##  1:  1.5 -0.151085119     24
##  2:  4.5 -0.131021735     24
##  3:  7.5 -0.052696624     24
##  4: 10.5 -0.052068821     24
##  5: 13.5 -0.057534745     24
##  6: 16.5 -0.057881841     24
##  7: 19.5 -0.062124974     24
##  8: 22.5 -0.085500161     24
##  9: 25.5 -0.008144267     16
## 10: 28.5 -0.027193375     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n     pval
##  1:      1.5      0.4451389     0.5059011 -0.05325172 144  0.029 *
##  2:      4.5      0.4754630     0.4959164 -0.02033100 216   0.3 :(
##  3:      7.5      0.4472222     0.4536325 -0.00790804 216  0.66 :(
##  4:     10.5      0.4680556     0.4252505  0.04656195 216  0.012 *
##  5:     13.5      0.4365741     0.4261071  0.01403922 216  0.49 :(
##  6:     16.5      0.4342593     0.4026857  0.02651931 216  0.17 :(
##  7:     19.5      0.4032407     0.3783452  0.02035500 216  0.32 :(
##  8:     22.5      0.4115741     0.3734210  0.03605929 216  0.058 .
##  9:     25.5      0.4180556     0.3586177  0.05729665 216 0.002 **
## 10:     28.5      0.3837963     0.3195975  0.05486978 216 0.004 **
##     time  error.diff shapes
##  1:  1.5 -0.05325172     24
##  2:  4.5 -0.02033100     16
##  3:  7.5 -0.00790804     16
##  4: 10.5  0.04656195     24
##  5: 13.5  0.01403922     16
##  6: 16.5  0.02651931     16
##  7: 19.5  0.02035500     16
##  8: 22.5  0.03605929     16
##  9: 25.5  0.05729665     24
## 10: 28.5  0.05486978     24

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.65081  -0.16600  -0.07689   0.21864   0.38438  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.29746    0.07745   3.841 0.000159 ***
## timeNorm     0.03979    0.04731   0.841 0.401279    
## obj.diff    -0.59239    0.08830  -6.709 1.52e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.03968561)
## 
##     Null deviance: 10.995  on 231  degrees of freedom
## Residual deviance:  9.088  on 229  degrees of freedom
## AIC: -85.242
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.6250000     0.8541813 -0.23116534 16   0.0013 **
##  2:      4.5      0.6375000     0.7984136 -0.16995810 24   0.0053 **
##  3:      7.5      0.6208333     0.7533950 -0.13245689 24     0.014 *
##  4:     10.5      0.6375000     0.7827081 -0.15599626 24   0.0079 **
##  5:     13.5      0.6250000     0.8239746 -0.20561865 24 4.4e-05 ***
##  6:     16.5      0.6375000     0.7813561 -0.15210779 24     0.027 *
##  7:     19.5      0.6541667     0.7252246 -0.07066985 24     0.14 :(
##  8:     22.5      0.6458333     0.7650575 -0.12329390 24     0.049 *
##  9:     25.5      0.6583333     0.7912822 -0.13403150 24   0.0072 **
## 10:     28.5      0.6166667     0.7394780 -0.11089775 24     0.042 *
##     time  error.diff shapes
##  1:  1.5 -0.23116534     24
##  2:  4.5 -0.16995810     24
##  3:  7.5 -0.13245689     24
##  4: 10.5 -0.15599626     24
##  5: 13.5 -0.20561865     24
##  6: 16.5 -0.15210779     24
##  7: 19.5 -0.07066985     16
##  8: 22.5 -0.12329390     24
##  9: 25.5 -0.13403150     24
## 10: 28.5 -0.11089775     24

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7128  -0.1799   0.0080   0.1979   0.6542  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.160601   0.040886   3.928 9.46e-05 ***
## timeNorm     0.003705   0.038216   0.097    0.923    
## obj.diff    -0.347965   0.054087  -6.433 2.39e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07289063)
## 
##     Null deviance: 51.554  on 666  degrees of freedom
## Residual deviance: 48.399  on 664  degrees of freedom
## AIC: 151.12
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5413043     0.6313063 -0.080414494 46   0.073 .
##  2:      4.5      0.5652174     0.6292224 -0.057371089 69   0.099 .
##  3:      7.5      0.5420290     0.5592216 -0.011452588 69   0.74 :(
##  4:     10.5      0.5463768     0.5820863 -0.022036607 69   0.57 :(
##  5:     13.5      0.5550725     0.5449914  0.012093320 69   0.72 :(
##  6:     16.5      0.5478261     0.5622564 -0.019457251 69   0.62 :(
##  7:     19.5      0.4942029     0.5766338 -0.086681165 69 0.0077 **
##  8:     22.5      0.4681159     0.5121072 -0.050443461 69   0.17 :(
##  9:     25.5      0.5014493     0.4988278 -0.003887111 69   0.93 :(
## 10:     28.5      0.5014493     0.4985043 -0.010272074 69    0.7 :(
##     time   error.diff shapes
##  1:  1.5 -0.080414494     16
##  2:  4.5 -0.057371089     16
##  3:  7.5 -0.011452588     16
##  4: 10.5 -0.022036607     16
##  5: 13.5  0.012093320     16
##  6: 16.5 -0.019457251     16
##  7: 19.5 -0.086681165     24
##  8: 22.5 -0.050443461     16
##  9: 25.5 -0.003887111     16
## 10: 28.5 -0.010272074     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61019  -0.15879   0.00778   0.17071   0.53696  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.13601    0.02536   5.362 1.08e-07 ***
## timeNorm     0.01638    0.02758   0.594    0.553    
## obj.diff    -0.23883    0.03902  -6.121 1.47e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0443567)
## 
##     Null deviance: 36.420  on 782  degrees of freedom
## Residual deviance: 34.598  on 780  degrees of freedom
## AIC: -212.38
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.5185185     0.5012163 0.020987089 54   0.54 :(
##  2:      4.5      0.4827160     0.4544613 0.033464592 81   0.19 :(
##  3:      7.5      0.4617284     0.4632778 0.001764609 81   0.95 :(
##  4:     10.5      0.5086420     0.4331719 0.087371264 81 0.0012 **
##  5:     13.5      0.4493827     0.4045805 0.051873659 81   0.053 .
##  6:     16.5      0.4851852     0.4351713 0.052762168 81   0.042 *
##  7:     19.5      0.4728395     0.4419957 0.028567591 81   0.25 :(
##  8:     22.5      0.4567901     0.3970833 0.064184236 81   0.019 *
##  9:     25.5      0.4283951     0.3766636 0.052786293 81   0.026 *
## 10:     28.5      0.4086420     0.3332279 0.073327560 81 0.0036 **
##     time  error.diff shapes
##  1:  1.5 0.020987089     16
##  2:  4.5 0.033464592     16
##  3:  7.5 0.001764609     16
##  4: 10.5 0.087371264     24
##  5: 13.5 0.051873659     16
##  6: 16.5 0.052762168     24
##  7: 19.5 0.028567591     16
##  8: 22.5 0.064184236     24
##  9: 25.5 0.052786293     24
## 10: 28.5 0.073327560     24

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79217  -0.22972  -0.02505   0.22977   0.66459  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.12306    0.03931   3.130  0.00188 ** 
## timeNorm     0.07410    0.04695   1.578  0.11536    
## obj.diff    -0.40016    0.03949 -10.133  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06460078)
## 
##     Null deviance: 31.012  on 376  degrees of freedom
## Residual deviance: 24.161  on 374  degrees of freedom
## AIC: 42.065
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.4961538     0.6433155 -0.16247940 26   0.013 *
##  2:      4.5      0.5461538     0.6907298 -0.14782189 39 0.0043 **
##  3:      7.5      0.5692308     0.7137322 -0.15795302 39 0.0025 **
##  4:     10.5      0.6000000     0.7008860 -0.10071842 39    0.04 *
##  5:     13.5      0.6128205     0.7134320 -0.09141725 39   0.027 *
##  6:     16.5      0.4897436     0.6151783 -0.15279698 39 0.0082 **
##  7:     19.5      0.5410256     0.6449734 -0.11891808 39    0.03 *
##  8:     22.5      0.6923077     0.7493528 -0.04695586 39   0.43 :(
##  9:     25.5      0.5512821     0.6507976 -0.08781013 39   0.085 .
## 10:     28.5      0.5589744     0.6282772 -0.05684813 39   0.24 :(
##     time  error.diff shapes
##  1:  1.5 -0.16247940     24
##  2:  4.5 -0.14782189     24
##  3:  7.5 -0.15795302     24
##  4: 10.5 -0.10071842     24
##  5: 13.5 -0.09141725     24
##  6: 16.5 -0.15279698     24
##  7: 19.5 -0.11891808     24
##  8: 22.5 -0.04695586     16
##  9: 25.5 -0.08781013     16
## 10: 28.5 -0.05684813     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78386  -0.16850  -0.01707   0.19827   0.81713  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.04390    0.02592   1.694   0.0907 .  
## timeNorm     0.05177    0.03442   1.504   0.1330    
## obj.diff    -0.25648    0.02693  -9.524   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07212868)
## 
##     Null deviance: 63.011  on 782  degrees of freedom
## Residual deviance: 56.260  on 780  degrees of freedom
## AIC: 168.31
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.4814815     0.5873966 -0.11643587 54     0.018 *
##  2:      4.5      0.5382716     0.6503420 -0.08355623 81 8.1e-05 ***
##  3:      7.5      0.4382716     0.4818131 -0.05279225 81     0.087 .
##  4:     10.5      0.5333333     0.6132263 -0.07281011 81   0.0075 **
##  5:     13.5      0.4580247     0.5555498 -0.08259856 81   7e-04 ***
##  6:     16.5      0.4296296     0.5255204 -0.08552546 81   0.0036 **
##  7:     19.5      0.5148148     0.5503347 -0.01335541 81     0.45 :(
##  8:     22.5      0.4172840     0.5170064 -0.09624598 81 0.00092 ***
##  9:     25.5      0.5728395     0.5963580 -0.01464263 81     0.43 :(
## 10:     28.5      0.5259259     0.5762030 -0.05335124 81     0.034 *
##     time  error.diff shapes
##  1:  1.5 -0.11643587     24
##  2:  4.5 -0.08355623     24
##  3:  7.5 -0.05279225     16
##  4: 10.5 -0.07281011     24
##  5: 13.5 -0.08259856     24
##  6: 16.5 -0.08552546     24
##  7: 19.5 -0.01335541     16
##  8: 22.5 -0.09624598     24
##  9: 25.5 -0.01464263     16
## 10: 28.5 -0.05335124     24

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.64422  -0.14739  -0.03019   0.20945   0.78069  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.007472   0.037241   0.201    0.841    
## timeNorm     0.007087   0.053454   0.133    0.895    
## obj.diff    -0.200869   0.040953  -4.905 1.57e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06439079)
## 
##     Null deviance: 20.029  on 289  degrees of freedom
## Residual deviance: 18.480  on 287  degrees of freedom
## AIC: 32.561
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.4050000     0.5766508 -0.16878595 20   0.017 *
##  2:      4.5      0.4066667     0.4839865 -0.06821507 30   0.077 .
##  3:      7.5      0.4166667     0.4678891 -0.04005955 30   0.24 :(
##  4:     10.5      0.3800000     0.3941140 -0.03236628 30   0.39 :(
##  5:     13.5      0.3333333     0.4690316 -0.13296057 30 0.0035 **
##  6:     16.5      0.3266667     0.4169360 -0.10259094 30   0.013 *
##  7:     19.5      0.3166667     0.4089949 -0.07762845 30   0.016 *
##  8:     22.5      0.4866667     0.5479898 -0.02155196 30   0.54 :(
##  9:     25.5      0.5000000     0.5677445 -0.03588560 30   0.26 :(
## 10:     28.5      0.3766667     0.4946265 -0.10348662 30 0.0062 **
##     time  error.diff shapes
##  1:  1.5 -0.16878595     24
##  2:  4.5 -0.06821507     16
##  3:  7.5 -0.04005955     16
##  4: 10.5 -0.03236628     16
##  5: 13.5 -0.13296057     24
##  6: 16.5 -0.10259094     24
##  7: 19.5 -0.07762845     24
##  8: 22.5 -0.02155196     16
##  9: 25.5 -0.03588560     16
## 10: 28.5 -0.10348662     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.42120  -0.06362  -0.00556   0.05334   0.53786  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.6422     0.3003   2.139 0.042026 *  
## timeNorm     -0.1947     0.1975  -0.986 0.333188    
## obj.diff     -1.0464     0.2620  -3.994 0.000475 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04893008)
## 
##     Null deviance: 2.2991  on 28  degrees of freedom
## Residual deviance: 1.2722  on 26  degrees of freedom
## AIC: -0.37187
## 
## Number of Fisher Scoring iterations: 2
## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable
##     time.bin subj.diff.mean obj.diff.mean  error.diff n    pval
##  1:      1.5      0.5000000     0.9955455 -0.49554554 2  0.5 :(
##  2:      4.5      0.5000000     0.9967907 -0.49693418 3 0.25 :(
##  3:      7.5      0.5333333     0.9009503 -0.39484271 3 0.25 :(
##  4:     10.5      0.6333333     0.9849214 -0.38031172 3 0.25 :(
##  5:     13.5      0.5333333     0.7416956 -0.18725631 3 0.25 :(
##  6:     16.5      0.5000000     0.9146985 -0.41136056 3 0.25 :(
##  7:     19.5      0.8333333     0.9409924 -0.06674186 3    1 :(
##  8:     22.5      0.1333333     0.7329528 -0.58342403 3 0.25 :(
##  9:     25.5      0.3333333     0.7462403 -0.35308187 3 0.25 :(
## 10:     28.5      0.5000000     0.3748959  0.12809629 3 0.25 :(
##     time  error.diff shapes
##  1:  1.5 -0.49554554     16
##  2:  4.5 -0.49693418     16
##  3:  7.5 -0.39484271     16
##  4: 10.5 -0.38031172     16
##  5: 13.5 -0.18725631     16
##  6: 16.5 -0.41136056     16
##  7: 19.5 -0.06674186     16
##  8: 22.5 -0.58342403     16
##  9: 25.5 -0.35308187     16
## 10: 28.5  0.12809629     16
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 9 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.67322  -0.15276  -0.07002   0.25118   0.53351  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.41770    0.04400   9.493   <2e-16 ***
## timeNorm     0.07669    0.04294   1.786   0.0747 .  
## obj.diff    -0.78113    0.04348 -17.966   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06642817)
## 
##     Null deviance: 56.712  on 492  degrees of freedom
## Residual deviance: 32.550  on 490  degrees of freedom
## AIC: 67.229
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.5235294     0.8070422 -0.292865784 34 2.8e-06 ***
##  2:      4.5      0.5588235     0.8193640 -0.278931373 51 7.3e-07 ***
##  3:      7.5      0.6431373     0.7451711 -0.129099152 51     0.023 *
##  4:     10.5      0.6392157     0.6749230 -0.051194535 51     0.32 :(
##  5:     13.5      0.6411765     0.7236293 -0.113615651 51     0.079 .
##  6:     16.5      0.6843137     0.7441892 -0.063755825 51     0.17 :(
##  7:     19.5      0.6098039     0.6923673 -0.089881684 51     0.039 *
##  8:     22.5      0.5784314     0.6650579 -0.093751223 51      0.07 .
##  9:     25.5      0.6137255     0.6053887  0.015440581 51     0.81 :(
## 10:     28.5      0.6019608     0.5833315 -0.003953812 51     0.92 :(
##     time   error.diff shapes
##  1:  1.5 -0.292865784     24
##  2:  4.5 -0.278931373     24
##  3:  7.5 -0.129099152     24
##  4: 10.5 -0.051194535     16
##  5: 13.5 -0.113615651     16
##  6: 16.5 -0.063755825     16
##  7: 19.5 -0.089881684     24
##  8: 22.5 -0.093751223     16
##  9: 25.5  0.015440581     16
## 10: 28.5 -0.003953812     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6433  -0.2296  -0.0120   0.2057   0.7430  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.18932    0.02365   8.006 3.25e-15 ***
## timeNorm     0.04122    0.03069   1.343     0.18    
## obj.diff    -0.46372    0.02876 -16.124  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06830381)
## 
##     Null deviance: 89.507  on 1014  degrees of freedom
## Residual deviance: 69.123  on 1012  degrees of freedom
## AIC: 161.39
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff   n      pval
##  1:      1.5      0.4000000     0.4893009 -0.091437861  70   0.033 *
##  2:      4.5      0.4895238     0.5313045 -0.049235027 105   0.13 :(
##  3:      7.5      0.4447619     0.4421186 -0.006272988 105   0.84 :(
##  4:     10.5      0.4619048     0.4280359  0.033577033 105   0.23 :(
##  5:     13.5      0.4561905     0.4304491  0.026094885 105   0.47 :(
##  6:     16.5      0.4257143     0.3735538  0.041884904 105   0.15 :(
##  7:     19.5      0.3742857     0.3204864  0.048698466 105   0.16 :(
##  8:     22.5      0.3552381     0.3052904  0.038894547 105    0.2 :(
##  9:     25.5      0.3866667     0.2849461  0.103892106 105 0.0016 **
## 10:     28.5      0.3666667     0.2590742  0.100961191 105  0.004 **
##     time   error.diff shapes
##  1:  1.5 -0.091437861     24
##  2:  4.5 -0.049235027     16
##  3:  7.5 -0.006272988     16
##  4: 10.5  0.033577033     16
##  5: 13.5  0.026094885     16
##  6: 16.5  0.041884904     16
##  7: 19.5  0.048698466     16
##  8: 22.5  0.038894547     16
##  9: 25.5  0.103892106     24
## 10: 28.5  0.100961191     24

{r plot.subjective.objective.difficulty.confidence.scale, echo=FALSE} # #-------------------------------------------------------------------------------------- # # SHOWING SUBJECTIVE VS OBJECTIVE DIFFICULTY (CONFIDENCE SCALE APPROACH) # #-------------------------------------------------------------------------------------- # # plot.subjective.difficulty <- function(DT,selGroup,title){ # # print(selGroup) # # # Lien entre mise normalisée et difficultée estimée (hard / easy effect) # obj.diff.quants = seq(0,1,1/16)#quantile(DT$obj.diff, probs=(seq(0,1,0.05))) # nb.bins = length(obj.diff.quants)-1 # subj.diff.med = numeric(nb.bins) # obj.diff.bin = numeric(nb.bins) # obj.diff.bin.cur = 0; # test.pvals = numeric(nb.bins) # conf.min = numeric(nb.bins) # conf.max = numeric(nb.bins) # nb.vals = numeric(nb.bins) # shapes = numeric(nb.bins) # delta.obj.subj = numeric(nb.bins) # hist(DT$obj.diff) # for(i in 1:nb.bins){ # #obj.diff.bin.cur = round(i/10,1) # #subj.diff = DT[round(obj.diff,1)==obj.diff.bin.cur]$subj.diff.mise # obj.diff.bin.cur = (obj.diff.quants[i] + obj.diff.quants[i+1])/2.0 # #subj.diff = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]]$subj.diff.mise # DTLoc = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]] # if(selGroup != "all") # DTLoc = DTLoc[niveau.group==selGroup] # DTLoc = DTLoc[,.(confiance.mean=mean(subj.diff.confiance)),by=IDjoueur] # subj.diff = DTLoc$confiance.mean # obj.diff.bin[i] = obj.diff.bin.cur # subj.diff.med[i] = NA # test.pvals[i] = NA # conf.min[i] = NA # conf.max[i] = NA # delta.obj.subj[i] = NA # shapes[i] = 16 # nb.vals[i] = length(subj.diff) # if(nb.vals[i] > 1){ # try.res = try(test.res <- wilcox.test(subj.diff,mu = obj.diff.bin.cur,conf.int=T)) # if (class(try.res) != "try-error"){ # #print(test.res) # #hist(subj.diff) # test.pvals[i] = format.pval.stars(test.res$p.value) # if(test.res$p.value < 0.05) # shapes[i] = 24 # #subj.diff.med[i] = mean(subj.diff) # subj.diff.med[i] = test.res$estimate # conf.min[i] = test.res$conf.int[1] # conf.max[i] = test.res$conf.int[2] # delta.obj.subj[i] = signif(subj.diff.med[i] - obj.diff.bin.cur,digit=2) # } # } # } # # #print table of pvalues # print(data.table(obj.diff.bin=obj.diff.bin,delta.obj.subj=delta.obj.subj,n=nb.vals,pval=test.pvals)) # # #summary # print("mean and sd of nb players per bin") # DTNbVals = data.table(nb = nb.vals, pval=test.pvals) # print(DTNbVals[!is.na(pval)]) # print(signif(mean(DTNbVals[!is.na(pval)]$nb),digits=3)) # print(signif(sd(DTNbVals[!is.na(pval)]$nb),digits=3)) # # #kernel smooth # subj.diff.smooth <- ksmooth(x=DT$obj.diff,y=DT$subj.diff.confiance,bandwidth = 0.2) # DTSmooth = data.table(x=subj.diff.smooth$x,y=subj.diff.smooth$y) # # DTPlot = data.table(obj.diff=obj.diff.bin,subj.diff=subj.diff.med, shapes=shapes) # # p = ggplot() + ggtitle(title) + # # geom_line(aes(x=DTPouet$x,y=DTPouet$y))+ # geom_point(aes(x=DTPlot$obj.diff,y=DTPlot$subj.diff),alpha = 1, size = 3, shape=DTPlot$shapes) + # xlim(0,1)+ # ylim(0,1)+ # geom_errorbar(aes(x=DTPlot$obj.diff, ymin=conf.min, ymax=conf.max), width=.01,color="red") + # geom_abline(intercept = 0, slope = 1, color="blue") + # xlab("Objective Difficulty") + ylab("Subjective Difficulty") + theme(text = element_text(size=15)) # # print(p) # } #

All tasks

{r plot.subjective.difficulty.all.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTAll,"all", "All tasks, all groups") # plot.subjective.difficulty(DTAll,"good", "All tasks, good") # plot.subjective.difficulty(DTAll,"medium", "All tasks, medium") # plot.subjective.difficulty(DTAll,"bad", "All tasks, bad") #

Motor task

{r plot.subjective.difficulty.motor.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTM,"all", "Motor, all") # plot.subjective.difficulty(DTM,"good", "Motor, good") # plot.subjective.difficulty(DTM,"medium", "Motor, medium") # plot.subjective.difficulty(DTM,"bad", "Motor, bad") #

Sensory task

{r plot.subjective.difficulty.sensory.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTS,"all","Sensory, all") # plot.subjective.difficulty(DTS,"good","Sensory, good") # plot.subjective.difficulty(DTS,"medium","Sensory, medium") # plot.subjective.difficulty(DTS,"bad","Sensory, bad") #

Logical task

{r plot.subjective.difficulty.logical.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTL,"all","Logical, all") # plot.subjective.difficulty(DTL,"good","Logical, good") # plot.subjective.difficulty(DTL,"medium","Logical, medium") # plot.subjective.difficulty(DTL,"bad","Logical, bad") #